The Program Committee of the 11th International Conference on Probabilistic Graphical Models (PGM 2022) announced the winner of the BayesFusion Best Student Paper Award in Almeria, Spain, on October 6. The winner is:
Enrico Giudice, Department of Mathematics and Computer Science, University of Basel, Switzerland, for the paper entitled The Dual PC Algorithm for Structure Learning, co-authored with Jack Kuipers and Giusi Moffa.
Our heartfelt congratulations!
Starting with version 2.0, SMILE fully supports Macs based on M series (Apple Silicon) ARM CPUs. The C++, Python and Java libraries are available as universal binaries, containing both ARM and x64 code. The wrapper for R is available as separate binaries for ARM and x64.
To download the libaries, go to https://download.bayesfusion.com.
SMILE 2.0 is now available. This version of the library supports discrete node outcomes based on numeric intervals or point values. Also, the metalog probability distribution can be used in equation node definitions.
The libraries for C++, Python, Java, R and .NET can be downloaded from https://download.bayesfusion.com. We also maintain repositories for use with Maven and pip, see the download website for more details.
GeNIe 4.0 is now available at https://download.bayesfusion.com.
Most important new features are:
- discrete nodes with outcomes based on numeric intervals or point values
- metalog distribution, including interactive metalog builder tool
- geospatial processing added, Esri ASCII raster grids supported
- new Distribution Visualizer window
How to use AWS Lambda with SMILE and more – see our new video:
BayesFusion’s Maven repository for jSMILE is now available. If you use jSMILE in a Maven-based project, you can reference the library directly in your POM file. For more details (including native library integration in POM), please refer to the Platforms and Wrappers/Java and jSMILE /Maven section in SMILE Wrappers Programmer’s Manual at our documentation website:
This is a 14-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams.
The course will take place through on-line meetings (Zoom).
9:00am-11:10am Eastern Time (6:00am-8:10am Pacific Time, 3:00pm-5:10pm Central European Time)
Monday, February 1, 2021
Tuesday, February 2, 2021
Wednesday, February 3, 2021
Thursday, February 4, 2021
Monday, February 8, 2021
Tuesday, February 9, 2021
Wednesday, February 10, 2021
Elementary college-level math and computer skills, basic data processing skills through tools such as Excel. No special prerequisites or knowledge of elements of decision-theoretic modeling or tools such as Bayesian networks. We will cover all that is required in the course. While all concepts covered in the course are general, we will use GeNIe to illustrate them. Tuition covers a 30-day GeNIe license for use during the course.
Course tuition fee $500 ($300 for students)
There is a minimum of 5 and a maximum of 20 participants.
For more information/to register: